Robot behavioral selection using q-learning

Abstract

Q-learning has often been used to learn primitive behaviors, or to coordinate a limited set of motor skills. However, the complexity of the algorithm increases exponentially with the number of states the robot can be in and the number of actions that it can take. Therefore, it is natural to try to reduce the number of states and actions in order to improve the efficiency of the algorithm. Robot behaviors and behavioral assemblages provide a good level of abstraction which could be used to speed up robot learning. Instead of coordinating a set of primitives, we use Q-learning to coordinate a set of well tested behavioral assemblages to accomplish a robot mission. The domain for our experiments is a simple intercept mission. This paper also explores the effects of imperfect perceptual algorithms on learning when this approach is used.